Real-life Example of AI

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There is a Fix for That

When machine learning meets healthcare analytics, the possibilities for true healthcare reforms are quite possible. This is because technology brings an added advantage not previously accessible. Machine learning is groundbreaking technology and many industries are taking advantage of it. From self-driving cars to coffee making robots, machine learning and artificial intelligence are expected to revolutionalize the everyday way of life. For the healthcare industry, this groundbreaking technology offers advantages such as more non-invasive surgeries, faster diagnosis, and preventive care. All this will lead to a high overall population health.

Machine learning is closely related to artificial intelligence, but they are distinguishable by the fact that AI is considered a machine that can carry out a task, while machine learning is an idea or program that has the capability of learning as it goes along. In the healthcare industry, they are looking for machine learning systems and models much more than that of AI and performing of physical tasks.

Healthcare organizations depend heavily on analytics to know and understand what is going on when providing care, when making goals for the organization and when making changes to better help the organization. Analytics, though, is seen as sort of an easy to procrastinate subject as well as the elephant in the room. Because it isn’t an in-your-face kind of matter, it can quickly be shuffled to the end of a meeting, yet its importance is noted as being the backbone of performance standards, reporting criteria and department development, it shouldn’t be overlooked by any stretch of the imagination.

If you look at these topics closely, it is almost as if they were meant to be together; machine learning and the possibility of continually acquiring more intelligence and the importance of healthcare analytics needing a constant help to understand inner workings seems like a perfect match.

Data is being generated every day and by billions of people. Not all that information is medically-based, but a great deal of it is. Of all the general health visits, emergency room visits, prescriptions, and imaging results, the petabytes-worth of data add up quickly, but that information isn’t always easy to compare and quantify. There are many software systems out there, specifically designed for healthcare organizations, that work to find patterns within the data that help lead to solutions for that organization. Some programs are better for certain types of organizations, but there is still a gap in that it still takes a person who understands the fundamentals of healthcare to identify where changes might best be implemented.

This task is becoming easier due to the fact that software programming is recognizing these patterns more and more, and relying on less input from a data analyst or department manager. The progression has an element of machine learning about it, but still lacks the autonomy for which the healthcare industry is in desperate need. Coupling the abilities that machine learning promises to an ever increasing base of knowledge is just what hospitals and doctors are searching for.

The two biggest issues plaguing the medical community are the ever-rising costs associated with care and the fact that we aren’t getting better care in accordance with those increased rates. If it is possible to implement machine learning in the realm of healthcare analytics, this could be seen as a game changer for many reasons.

Better recognition of organizational patterns and trends

Better data-driven decisions

Better inventory and staff control

Better understanding of goals based on current demands

More accurate reporting both internally and externally

All of these done on a timelier basis

Each one of these possibilities works to do either or both the stated objectives of lowering costs or providing better care. But it would be done more efficiently than it is presently being conducted and could be done more accurately, especially if the machine language system was able to tap into vast amounts of information to draw out existing healthcare models.

Many other industries and businesses have successfully married the concepts of products or services and machine language. Healthcare is working hard to find the ways to incorporate the complexities of healthcare data and machine language, and have started by doing what they can to make it possible. These baby steps are important to growing the technology, so what exists now may morph into something completely different, but it started out somewhere, with some really good ideas and intentions. When machine learning meets healthcare analytics in more and more applications and situations, the results will prove to be exciting, helpful and lead to better care for each one of us. Be sure to watch for the transformations as they quickly become standard practices in the healthcare communities.